A dynamic risk model for inpatient falls. (1st September 2018)

• Record Type: Journal Article
• Authors: Choi, Yoonyoung; Staley, Benjamin; Henriksen, Carl; Xu, Dandan; Lipori, Gloria; Brumback, Babette; Winterstein, Almut G.
• Is Part Of: American journal of health-system pharmacy. Volume 75:Number 17(2019)
• Page Start: 1293
• Page End: 1303
• Publication Date: 2018-09-01
• Subjects: electronic health record -- fall risk -- Morse Fall Scale -- prediction model -- risk score; Hospital pharmacies -- United States -- Periodicals; 615.1
• DOI: 10.2146/ajhp180013
• Languages: English
• ISSNs: 1079-2082

Abstract

Abstract: Purpose: Construction and validation of a fall risk prediction model specific to inpatients receiving fall risk–increasing drugs (FRIDs) are described. Methods: In a retrospective cohort study of 75, 036 admissions to 2 hospitals over a designated 22-month period that involved FRID exposure during the first 5 hospital days, factors influencing fall risk were investigated via logistic regression. The resultant risk prediction model was internally validated and its performance compared with that of a model based on Morse Fall Scale (MFS) scores. Results: A total of 220, 904 patient-days of FRID exposure were evaluated. The three most commonly administered FRIDs were oxycodone (given on 79, 697 patient-days, 36.08%), morphine (52, 427, 23.73%) and hydromorphone (42, 063, 19.04%). Within the 90th percentile of modeled risk scores, 144 of the 466 documented falls (30.9%) were captured by the developed risk prediction model (unbiased C statistic, 0.69), as compared with 94 falls (20.2%) captured using the MFS model (unbiased C statistic, 0.62). Strong predictors of inpatient falls included a history of falling (odds ratio [OR], 1.99; 95% confidence interval (CI), 1.42–2.80); overestimation of ability to ambulate (OR, 1.53; 95% CI, 1.12–2.09); and "comorbidity predisposition, " a composite measure encompassing a history of falling and 11 past diagnoses (OR, 1.60; 95% CI, 1.30–1.97). Conclusion: The proposed risk model for inpatient falls achieved superior predictive performance when compared with the MFS model. All risk factors were operationalized from discrete electronic health record fields, allowing full automation of real-time identification of high-risk patients.